code: def train(
      network_def,
      online_params,
      target_params,
      optimizer,
      optimizer_state,
      states,
      actions,
      next_states,
      rewards,
      terminals,
      cumulative_gamma,
      dr3_coefficient,
      cql_coefficient,
  ):
      """Run the training step."""
  
      def loss_fn(params, bellman_target):
          def q_online(state):
              return network_def.apply(params, state)
  
          model_output = jax.vmap(q_online)(states)
          representations = jnp.squeeze(model_output.representation)
          next_states_model_output = jax.vmap(q_online)(next_states)
          next_state_representations = jnp.squeeze(
              next_states_model_output.representation
          )
          dr3_loss = compute_dr3_loss(
              representations, next_state_representations
          )
  
          # Q-learning loss
          q_values = jnp.squeeze(model_output.q_values)
          replay_chosen_q = jax.vmap(lambda x, y: x[y])(
              q_values, actions
          )
          bellman_loss = jnp.mean(
              jax.vmap(losses.huber_loss)(
                  bellman_target, replay_chosen_q
              )
          )
  
          # CQL Loss
          cql_loss = jnp.mean(
              jax.scipy.special.logsumexp(q_values, axis=-1)
              - replay_chosen_q
          )
          loss = (
              bellman_loss
              + dr3_coefficient * dr3_loss
              + cql_coefficient * cql_loss
          )
  
          q_vals_data = jnp.mean(replay_chosen_q)
          q_vals_pi = jnp.mean(jnp.max(q_values, axis=1))
  
          return jnp.mean(loss), (
              bellman_loss,
              dr3_loss,
              cql_loss,
              q_vals_data,
              q_vals_pi,
          )
  
      def q_target(state):
          return network_def.apply(target_params, state)
  
      # Making a temp function for measuring the bellman loss
      def q_online_temp(state):
          return network_def.apply(online_params, state)
  
      bellman_target = base_dqn_agent.target_q(
          q_target, next_states, rewards, terminals, cumulative_gamma
      )
      grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
      (loss, component_losses), grad = grad_fn(
          online_params, bellman_target
      )
  
      # Compute actual Bellman targets
      actual_bellman_target = base_dqn_agent.target_q(
          q_online_temp,
          next_states,
          rewards,
          terminals,
          cumulative_gamma,
      )
      _, (actual_bellman_loss, _, _, _, _) = loss_fn(
          online_params, actual_bellman_target
      )
      (
          bellman_loss,
          dr3_loss,
          cql_loss,
          q_vals_data,
          q_vals_pi,
      ) = component_losses
      updates, optimizer_state = optimizer.update(grad, optimizer_state)
      online_params = optax.apply_updates(online_params, updates)
      return (
          optimizer_state,
          online_params,
          loss,
          bellman_loss,
          dr3_loss,
          cql_loss,
          actual_bellman_loss,
          q_vals_data,
          q_vals_pi,
      )
  
file_path: dopamine/dopamine/labs/offline_rl/jax/offline_dr3_agent.py
imports: import functools
  from absl import logging
  from dopamine.jax import losses
  from dopamine.jax.agents.dqn import dqn_agent
  from dopamine.labs.offline_rl.jax import offline_dqn_agent
  from dopamine.metrics import statistics_instance
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: train(network_def, online_params, target_params, optimizer, optimizer_state, states, actions, next_states, rewards, terminals, cumulative_gamma, dr3_coefficient, cql_coefficient) and returns: optimizer_state,  online_params,  loss,  bellman_loss,  dr3_loss,  cql_loss,  actual_bellman_loss,  q_vals_data,  q_vals_pi, jnp,  bellman_loss,  dr3_loss,  cql_loss,  q_vals_data,  q_vals_pi, network_def, network_def, network_def